Consistent patterns of maturity and density-dependent growth among populations of walleye (Sander vitreus): application of the growing degree-day metric
Bibliographic record
Abstract
Growing degree-days (GDD, °C·days) are an index of ambient thermal energy that relates directly to an ectotherm’s cumulative metabolism but is rarely used to describe growth and development in fish. We applied GDD to length and maturity data from 416 populations of walleye ( Sander vitreus ) from Ontario and Quebec, Canada (mean annual GDD = 1200 to 2300 °C·days). On average, males matured after they had experienced 6900 °C·days and reached 350 mm total length (L) (n = 77 populations), and females matured after 10 000 °C·days and at 450 mm L (n = 70). Across 143 populations, GDD accounted for up to 96% of the variation in the length of immature walleye but also revealed a twofold difference in growth rate that was indicative of variation in food availability. When applied to data from eight populations in which walleye abundances have changed dramatically over time, GDD revealed a 1.3-fold increase in immature growth rate when abundance was low compared with when it was high. Our results both demonstrate the explanatory power of GDD with respect to fish growth and maturity and inform the development of regional management strategies for walleye.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".